Amazon Web Services (AWS) is the world’s leading cloud computing platform that offers a wide range of cloud-based services, including computing power, storage, networking, databases, machine learning, and security. AWS enables businesses, startups, and enterprises to build scalable, cost-effective, and secure applications without having to invest in on-premises infrastructure. With over 200 fully featured services across data centers globally, AWS is used by millions of organizations to enhance operational efficiency and drive innovation.

What is AWS?

AWS is a comprehensive cloud computing platform developed by Amazon that provides Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) solutions. AWS offers a pay-as-you-go pricing model, allowing organizations to only pay for the resources they use. It supports businesses across various industries, including healthcare, finance, education, gaming, and artificial intelligence.

Key Characteristics of AWS:

• Highly Scalable: Offers automatic scaling for workloads and applications.
• Secure & Compliant: Provides enterprise-level security with compliance certifications.
• Cost-Effective: Reduces IT costs by offering flexible pricing options.
• Global Infrastructure: Spans multiple availability zones and regions worldwide.
• Innovative Technologies: Supports AI, IoT, blockchain, and analytics.

Top 10 Use Cases of AWS

1. Website Hosting & Content Delivery
   • AWS enables businesses to host static and dynamic websites with services like Amazon S3, Amazon EC2, and AWS CloudFront.
2. Big Data Analytics
   • AWS services such as Amazon Redshift, AWS Glue, and AWS Athena help businesses process and analyze large datasets efficiently.
3. Machine Learning & AI
   • AWS provides pre-trained AI models and machine learning frameworks through services like Amazon SageMaker, AWS DeepLens, and AWS Lex.
4. Internet of Things (IoT)
   • AWS IoT Core and AWS Greengrass allow organizations to securely connect and manage IoT devices at scale.
5. Cloud Storage & Backup Solutions
   • Amazon S3, AWS Glacier, and AWS Backup provide reliable storage and backup solutions with high availability.
6. DevOps & Continuous Integration/Continuous Deployment (CI/CD)
   • AWS CodePipeline, AWS CodeBuild, and AWS Lambda facilitate CI/CD pipelines for faster application development and deployment.
7. Enterprise Applications & ERP Solutions
   • Businesses use AWS to host ERP software like SAP and Oracle, reducing costs and increasing efficiency.
8. Gaming & Media Streaming
   • AWS services like Amazon GameLift and AWS Elemental enable seamless online gaming and video streaming experiences.
9. Disaster Recovery & Business Continuity
   • AWS ensures data redundancy and business continuity through multi-region backup and recovery solutions.
10. Blockchain & Cryptocurrency

• AWS supports blockchain solutions for secure transactions using Amazon Managed Blockchain and AWS Quantum Ledger Database.

Features of AWS

1. Elastic Compute Cloud (EC2) – Scalable virtual servers for hosting applications and workloads.
2. Simple Storage Service (S3) – Secure and scalable object storage for backup, archive, and data sharing.
3. AWS Lambda – Serverless computing for running applications without managing infrastructure.
4. AWS CloudFormation – Automates infrastructure provisioning using templates.
5. Amazon RDS (Relational Database Service) – Fully managed databases like MySQL, PostgreSQL, and Oracle.
6. AWS Identity and Access Management (IAM) – Controls access permissions for AWS services and resources.
7. AWS Auto Scaling – Automatically scales applications to handle varying traffic loads.
8. Amazon DynamoDB – NoSQL database for high-performance applications.
9. AWS Virtual Private Cloud (VPC) – Secure cloud networking and private IP address management.
10. Amazon CloudWatch – Monitoring and logging service for AWS applications and infrastructure.

How AWS Works and Architecture

1. AWS Global Infrastructure

• AWS operates in multiple regions, availability zones (AZs), and edge locations worldwide.
• Each region consists of multiple AZs to ensure fault tolerance and disaster recovery.

2. Compute Services

• AWS EC2 instances provide virtual machines for running applications.
• AWS Lambda offers serverless computing to run code without managing servers.

3. Storage Services

• AWS S3 provides scalable object storage.
• Amazon EBS (Elastic Block Store) is used for persistent storage attached to EC2 instances.

4. Networking & Content Delivery

• AWS VPC allows users to create private cloud networks.
• AWS CloudFront delivers content with low latency using a global CDN.

5. Security & Compliance

• AWS IAM ensures secure access control.
• AWS Shield provides protection against DDoS attacks.

How to Install AWS

It seems like you’re asking how to install AWS CLI (Amazon Web Services Command Line Interface) or use AWS resources programmatically via code, but the phrase “AWS in coe” isn’t entirely clear. I’ll assume you’re referring to installing and configuring the AWS CLI or interacting with AWS services using programming code (such as Python, Terraform, etc.).

1. Installing AWS CLI

The AWS CLI (Command Line Interface) is a tool that allows you to interact with AWS services from your terminal. Here’s how to install AWS CLI:

Step 1: Install AWS CLI (Version 2)

For Windows:

1. Download the AWS CLI installer for Windows from AWS CLI download page.
2. Run the installer and follow the prompts.

For macOS:

You can install AWS CLI using Homebrew:

brew install awscli

Alternatively, use the official installer:

curl "https://awscli.amazonaws.com/awscli-exe-macos-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

For Linux (Ubuntu/Debian-based):

To install AWS CLI on Linux, run:

# Download and install AWS CLI v2
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

Verify Installation:

After installation, verify that AWS CLI is installed properly by running:

aws --version

You should see an output similar to:

aws-cli/2.x.x Python/3.x.x Linux/4.x.x

2. Configure AWS CLI

Once installed, you need to configure the AWS CLI with your AWS credentials (Access Key and Secret Key).

aws configure

You’ll be prompted to enter the following:

• AWS Access Key ID: You can find this in your AWS Console under IAM (Identity and Access Management).
• AWS Secret Access Key: This will also be available in the IAM section.
• Default Region Name: This is the region you typically use, e.g., us-west-2.
• Default Output Format: Usually set to json, but you can choose text or table.

3. Install AWS SDK (For Programming Code)

If you’re interacting with AWS services programmatically, you can use AWS SDKs. Here’s how to use Python (boto3) as an example.

Step 1: Install boto3 (AWS SDK for Python)

You can install boto3, the AWS SDK for Python, using pip:

pip install boto3

Step 2: Example Python Code to Interact with AWS

Once boto3 is installed, you can write Python code to interact with AWS services.

Here’s an example Python script that lists all EC2 instances in your AWS account:

import boto3

# Create a session using your AWS credentials
ec2 = boto3.client('ec2')

# Describe EC2 instances
response = ec2.describe_instances()

# Print instance details
for reservation in response['Reservations']:
    for instance in reservation['Instances']:
        print(f"ID: {instance['InstanceId']}, Type: {instance['InstanceType']}, State: {instance['State']['Name']}")

Step 3: Verify Authentication

Before using the SDK, ensure you’re authenticated using AWS CLI with the aws configure command or by setting up your credentials file.

Alternatively, you can provide your AWS Access Key ID and Secret Access Key programmatically using:

import boto3

# Use AWS access keys directly (if not using configured profile)
ec2 = boto3.client('ec2', aws_access_key_id='your-access-key',
                  aws_secret_access_key='your-secret-key', region_name='us-west-2')

However, using IAM roles and AWS CLI configuration is the recommended and safer approach.

4. Automate AWS Infrastructure with Terraform

You can use Terraform to provision and manage AWS resources. Here’s an example of provisioning an EC2 instance with Terraform:

Step 1: Install Terraform

Download and install Terraform from the official site.

For Linux (Ubuntu):

sudo apt-get update
sudo apt-get install terraform

For macOS:

brew install terraform

Step 2: Configure Terraform to Use AWS

Create a main.tf file to configure an AWS provider and resource.

# Configure AWS provider
provider "aws" {
  region = "us-west-2"
}

# Provision an EC2 instance
resource "aws_instance" "example" {
  ami           = "ami-0c55b159cbfafe1f0"  # Use your preferred AMI ID
  instance_type = "t2.micro"

  tags = {
    Name = "MyInstance"
  }
}

Step 3: Apply Terraform Configuration

Initialize and apply the Terraform configuration:

terraform init
terraform apply

This will provision the EC2 instance on AWS based on the configuration.

5. Monitor and Manage AWS with CloudWatch and CloudTrail

You can use CloudWatch to monitor AWS services and CloudTrail to log API activity.

For example, using AWS CLI to create a CloudWatch alarm:

aws cloudwatch put-metric-alarm --alarm-name "HighCPUAlarm" \
  --metric-name "CPUUtilization" --namespace "AWS/EC2" \
  --statistic "Average" --period 300 --threshold 80 \
  --comparison-operator "GreaterThanThreshold" \
  --dimensions "Name=InstanceId,Value=i-12345678" \
  --evaluation-periods 2 --alarm-actions arn:aws:sns:us-west-2:123456789012:MyTopic

This creates an alarm that triggers an SNS notification if CPU utilization exceeds 80%.

Basic Tutorials of AWS: Getting Started

Step 1: Create an EC2 Instance

1. Log in to the AWS Management Console.
2. Navigate to EC2 > Launch Instance.
3. Select an Amazon Machine Image (AMI) (e.g., Ubuntu, Windows Server).
4. Choose an Instance Type (e.g., t2.micro for free tier).
5. Configure security groups and launch the instance.

Step 2: Create an S3 Bucket

1. Go to S3 Service in AWS.
2. Click Create Bucket, set a unique bucket name, and choose a region.
3. Configure permissions and upload files.

Step 3: Deploy a Serverless Function with AWS Lambda

1. Open AWS Lambda from the AWS Console.
2. Click Create Function and select Author from Scratch.
3. Choose a runtime (e.g., Python, Node.js).
4. Upload your function code and deploy.

Step 4: Set Up a CloudWatch Monitoring Dashboard

1. Go to Amazon CloudWatch.
2. Click Create Dashboard.
3. Add widgets for CPU Usage, Memory Utilization, and Network Metrics.

The post What is AWS and Use Cases of AWS? appeared first on Artificial Intelligence.

Amazon Web Services (AWS) Security Hub is a centralized security management service that provides a comprehensive view of your security posture across all your AWS accounts. It collects, aggregates, and prioritizes security findings from AWS services and third-party tools, helping organizations monitor compliance, detect threats, and respond to incidents efficiently. With AWS Security Hub, security teams can streamline their operations and maintain consistent security standards across their cloud environments.

What is AWS Security Hub?

AWS Security Hub is a cloud-native security service that consolidates security findings from multiple AWS services, such as Amazon GuardDuty, Amazon Inspector, and AWS Config, as well as third-party security tools. It uses built-in security standards and frameworks to assess your environment and provide actionable insights. AWS Security Hub enables continuous monitoring and helps organizations improve their security posture in real time.

Key Characteristics of AWS Security Hub:

• Centralized Security View: Provides a single dashboard to view and manage security findings across AWS accounts.
• Automated Compliance Checks: Evaluates your environment against security frameworks like CIS AWS Foundations Benchmark and PCI DSS.
• Integration Capabilities: Seamlessly integrates with AWS services and third-party security solutions.
• Customizable Insights: Allows customization of security rules and alerts to meet specific organizational requirements.

Top 10 Use Cases of AWS Security Hub

1. Centralized Security Management
   • Consolidates security findings from AWS services and third-party tools into a unified view.
2. Threat Detection and Response
   • Identifies and prioritizes security threats by integrating with services like Amazon GuardDuty and AWS WAF.
3. Compliance Monitoring
   • Continuously monitors and evaluates your environment against compliance standards like CIS, PCI DSS, and AWS Foundational Security Best Practices.
4. Multi-Account Security Management
   • Simplifies security management across multiple AWS accounts and regions.
5. Cloud Resource Monitoring
   • Detects misconfigurations and vulnerabilities in AWS resources, such as S3 buckets, EC2 instances, and IAM roles.
6. Incident Investigation and Forensics
   • Provides detailed security findings for incident analysis and root cause determination.
7. Integration with SIEM Tools
   • Integrates with SIEM solutions like Splunk and Datadog for enhanced security event analysis.
8. Automation and Remediation
   • Automates security tasks using AWS Lambda to remediate identified issues.
9. Custom Security Rules
   • Enables the creation of custom security rules tailored to organizational needs.
10. Real-Time Alerts
    • Generates real-time alerts and notifications for critical security findings.

Features of AWS Security Hub

1. Centralized Dashboard – Provides a unified view of security findings across AWS accounts and regions.
2. Automated Security Checks – Continuously evaluates your environment against best practices and compliance frameworks.
3. Integration with AWS Services – Works seamlessly with GuardDuty, Inspector, AWS Config, and more.
4. Third-Party Integration – Supports integration with leading security tools like Trend Micro, McAfee, and Palo Alto Networks.
5. Custom Actions – Allows automated responses to security findings using AWS Lambda functions.
6. Consolidated Findings – Aggregates findings from various sources to reduce noise and prioritize critical issues.
7. Multi-Account Support – Simplifies security management for organizations with multiple AWS accounts.
8. Compliance Frameworks – Includes pre-built frameworks such as CIS AWS Foundations Benchmark and PCI DSS.
9. Detailed Reporting – Offers detailed insights and recommendations for security improvements.
10. Scalable and Cost-Effective – Scales with your AWS environment and operates on a pay-as-you-go pricing model.

How AWS Security Hub Works and Architecture

1. Data Collection

• AWS Security Hub collects security findings from AWS services like GuardDuty, AWS Config, and Inspector, as well as third-party tools via APIs and integrations.

2. Findings Aggregation

• Findings are aggregated, normalized, and deduplicated to reduce noise and provide a clear view of security risks.

3. Compliance Evaluation

• The service automatically checks your resources against compliance frameworks and provides detailed results and recommendations.

4. Prioritization and Visualization

• Security Hub prioritizes findings based on severity and risk level, displaying them in a centralized dashboard.

5. Integration and Automation

• Integrates with AWS Lambda and other AWS services to automate responses and remediation for identified security issues.

How to Install AWS Security Hub

AWS Security Hub is a comprehensive security service that provides centralized visibility into the security state of your AWS environment. It helps aggregate, organize, and prioritize security findings from various AWS services (e.g., AWS GuardDuty, AWS Inspector, and AWS Macie) as well as from third-party security solutions.

To install and configure AWS Security Hub programmatically, you can use AWS CLI commands, AWS SDKs, or AWS CloudFormation templates. Below are the steps and code snippets to help automate the installation and configuration of AWS Security Hub using the AWS CLI and CloudFormation.

1. Prerequisites

Before starting, make sure you have the following:

• AWS CLI installed and configured with your credentials.
• IAM Permissions: Ensure you have the necessary IAM permissions to create and configure AWS Security Hub (e.g., securityhub:EnableSecurityHub, securityhub:DescribeHub, etc.).

2. Enable AWS Security Hub Using AWS CLI

To enable AWS Security Hub, you can use the AWS CLI. Here’s how you can enable it programmatically.

Step 1: Enable AWS Security Hub

Use the following AWS CLI command to enable AWS Security Hub in your AWS account:

aws securityhub enable-security-hub

This command enables AWS Security Hub in your current AWS region. You should see a confirmation output indicating that the service has been enabled.

Step 2: Enable Security Standards

You can enable various security standards such as AWS Foundational Security Best Practices, CIS AWS Foundations Benchmark, or others. For example, to enable the AWS Foundational Security Best Practices:

aws securityhub enable-security-standards --standards-arn arn:aws:securityhub:us-east-1::standards/aws-foundational-security-best-practices/v/1.0.0

This enables the AWS Foundational Security Best Practices standard in AWS Security Hub.

Step 3: Enable AWS Config Integration (Optional)

If you want to integrate AWS Config with Security Hub to collect configuration and compliance data:

aws securityhub enable-import-findings-from-securityhub --import-findings

3. Set Up AWS Security Hub Using AWS SDK

You can also use AWS SDKs (e.g., Python boto3) to automate the process of enabling and configuring AWS Security Hub.

Step 1: Install the AWS SDK (boto3 for Python)

If you’re using Python, install the boto3 library:

pip install boto3

Step 2: Enable AWS Security Hub using boto3

Here’s an example using Python and boto3 to enable AWS Security Hub:

import boto3

# Create a SecurityHub client
client = boto3.client('securityhub')

# Enable AWS Security Hub
response = client.enable_security_hub()

# Print the response
print(response)

This script uses the AWS SDK for Python to enable Security Hub in your AWS account.

Step 3: Enable Security Standards using boto3

Here’s how you can enable the AWS Foundational Security Best Practices standard programmatically:

# Enable AWS Foundational Security Best Practices
response = client.enable_security_standards(
    StandardsArn='arn:aws:securityhub:us-east-1::standards/aws-foundational-security-best-practices/v/1.0.0'
)

print(response)

This script enables the AWS Foundational Security Best Practices standard for security assessments.

4. Set Up AWS Security Hub Using CloudFormation

You can also enable and configure AWS Security Hub via AWS CloudFormation. Below is an example CloudFormation template to enable Security Hub.

Step 1: CloudFormation Template to Enable Security Hub

AWSTemplateFormatVersion: '2010-09-09'
Resources:
  EnableSecurityHub:
    Type: 'AWS::SecurityHub::Hub'
    Properties:
      Tags:
        Name: 'SecurityHubSetup'

This CloudFormation template enables Security Hub in the AWS environment.

Step 2: Deploy CloudFormation Template Using AWS CLI

Once you have your CloudFormation template (securityhub-setup.yaml), you can deploy it using the following command:

aws cloudformation create-stack --stack-name EnableSecurityHubStack --template-body file://securityhub-setup.yaml

This will create a CloudFormation stack that enables AWS Security Hub.

5. Integrate Findings from Other AWS Services

Once you have enabled Security Hub, you can start aggregating findings from other services like AWS GuardDuty, AWS Macie, and AWS Inspector.

Step 1: Enable GuardDuty Findings in Security Hub

If you have Amazon GuardDuty enabled, you can automatically send findings from GuardDuty to Security Hub:

aws securityhub enable-import-findings-from-source --source-type "GuardDuty"

Step 2: Enable Macie Findings in Security Hub

If you are using Amazon Macie for sensitive data discovery, you can send Macie findings to Security Hub:

aws securityhub enable-import-findings-from-source --source-type "Macie"

6. View Security Hub Findings

Once everything is set up, you can view the security findings using the AWS Management Console or by querying Security Hub using AWS CLI or boto3.

For example, to list findings using AWS CLI:

aws securityhub get-findings

Or using boto3:

# Retrieve findings from Security Hub
response = client.get_findings()

# Print findings
for finding in response['Findings']:
    print(finding)

7. Enable Security Hub in Multiple Regions

If you want to enable AWS Security Hub across multiple regions, you need to manually enable it in each region or use automation scripts to deploy across your regions.

For example, with AWS CLI, you can set the --region flag for each region:

aws securityhub enable-security-hub --region us-west-2
aws securityhub enable-security-hub --region eu-west-1

Basic Tutorials of AWS Security Hub: Getting Started

Step 1: Enable Security Hub

• Go to the AWS Management Console, search for Security Hub, and enable the service.

Step 2: Add AWS Services

1. Navigate to Settings in the Security Hub console.
2. Enable integrations with services like GuardDuty, AWS Config, and Inspector.

Step 3: Configure Compliance Checks

• Select and enable security frameworks (e.g., CIS AWS Foundations Benchmark) for continuous compliance monitoring.

Step 4: View Security Findings

• Access the Findings tab to view aggregated security alerts and prioritize critical issues.

Step 5: Automate Actions

• Use AWS Lambda to create automated workflows for responding to specific findings.

Step 6: Generate Reports

• Use the Compliance tab to generate detailed compliance reports for your AWS environment.

The post What is Amazon Web Services (AWS) Security Hub and Its Use Cases? appeared first on Artificial Intelligence.

In 1960, Nobel Laureate and American physicist Eugene Wigner wrote about the ‘unreasonable effectiveness of mathematics in natural sciences’. Mathematics is called the language of nature for a reason. That’s why the ‘Is math invented or discovered?’ debate never gets old. Mathematics exerts its influence on literally every field.

Mathematics is also the building block of machine learning models. ML practitioners use mathematics to analyse a problem, pick out better heuristics, and club both to generate an answer. Despite the critical role mathematics plays in machine learning, even state-of-art models struggle at maths.

A new study by the researchers at the University of California, Berkeley, have now introduced the MATH dataset. The team said the dataset provides a detailed assessment of a model’s mathematical ability across difficulties and subjects.

What Is MATH Dataset?

The MATH dataset consists of 12,500 problems taken from various high school mathematics competitions. The dataset measures the problem-solving ability of large and general-purpose language models. A machine learning model generates a sequence for a given problem from the MATH dataset and encodes the final answer. 

MATH problems are labelled from 1 to 5, depending on the difficulty level and span across seven subjects, including geometry, number theory, algebra calculus, statistics, and linear algebra. For problems of geometry, diagrams can be specified with the Asymptote language.

Since step-by-step solutions also accompany the problems, language models can learn to answer questions they haven’t been exposed to before. The step-by-step approach allows models to perform intermediate computations instead of giving the final answer immediately. 

Recognising the need to train the model on maths fundamentals before exposing to MATH that cover advanced problem-solving techniques, the team also released the Auxiliary Mathematics Problems and Solutions (AMPS). The ‘pretraining corpus’ has over 100,000 problems from Khan Academy with solutions and 5 million problems, based on 100 hand-designed modules, generated using Mathematica scripts. 

Results

When the MATH dataset was tested for large language models, including GPT-3, the accuracies were found to be abysmally low, ranging from 2.9 percent to 6.9 percent. However, on the flip side, the models achieved up to 15 percent accuracy on the easiest level. When evaluated on humans, a PhD student with no specialisation in Mathematics attained 40 percent, while a three-time Olympiad gold medalist scored 90 percent.

Further, having the models generate a step-by-step solution before producing the final answer reduced accuracy. This was because, while many of these steps were related to the question, they were not logical. 

The researchers found simply increasing the amount of training time, and the parameters proved extremely costly, although they did improve performance in a few cases. The researchers have open-sourced both MATH and AMPS to encourage and facilitate further research in this direction.

Predecessors

OpenAI recently introduced GPT-f, an automated prover and proof assistant for the Metamath formalisation language. Metamath is a language that expresses theorems in abstract mathematics along with proofs that a computer program can validate. 

Last year, Facebook built an AI system that can solve complex mathematical problems using symbolic reasoning. The team gave a system to represent mathematical expressions as a language and then treating the solutions as a translation problem for sequence-to-sequence neural networks.

Wrapping Up

While most other text-based tasks are already nearly solved by enormous Transformers, MATH is notably different. We showed that accuracy is slowly increasing and, if trends continue, the community will need to discover conceptual and algorithmic breakthroughs to attain strong performance on MATH, the researchers stated.

The post Domo Achieves AWS Machine Learning Competency Status in Applied AI appeared first on Artificial Intelligence.

Latest released the research study on Global Cloud Microservices Market, offers a detailed overview of the factors influencing the global business scope. Cloud Microservices Market research report shows the latest market insights, current situation analysis with upcoming trends and breakdown of the products and services. The report provides key statistics on the market status, size, share, growth factors of the Cloud Microservices. The study covers emerging player’s data, including: competitive landscape, sales, revenue and global market share of top manufacturers are AWS (United States),CA Technologies (United States),Contino (United Kingdom),CoScale (Belgium),IBM (United States),Idexcel (United States),Infosys (India),Kontena (Finland),Macaw Software (United States),Marlabs (United States),Microsoft (United States),Netifi (United States),NGINX (United States),OpenLegacy (United States),Oracle (United States),Pivotal Software (United States).

Definition:
A cloud microservices is known as a software development technique and a collection of services which are loosely coupled. Cloud Microservices is a method of developing software systems which tries to emphasis on developing single-function modules with the well-defined operations and interfaces. It is also known as the microservice architecture. Cloud microservices are gaining popularity among companies that need greater scalability and agility. In businesses across the industries, i.e., from telecommunications and retail to the financial services and manufacturing, IT teams are choosing cloud microservices, to develop new applications and break down monoliths

Keep yourself up-to-date with latest market trends and changing dynamics due to COVID Impact and Economic Slowdown globally. Maintain a competitive edge by sizing up with available business opportunity in Cloud Microservices Market various segments and emerging territory.

Market Trend:
Digital transformations

Market Drivers:
Proliferation of the microservices architecture
Customer-oriented business

Restraints:
Security and compliance

The Global Cloud Microservices Market segments and Market Data Break Down are illuminated below:
by Type (Stateless, Persistence, Aggregator), Industry (BFSI, IT and ITeS, Telecommunications, Government, Healthcare, Retail and eCommerce, Media and Entertainment, Transportation and Logistics, Manufacturing, Others (Education, Energy & Utilities, and Travel & Hospitality)), Services (Consulting services, Integration services, Training, support and maintained services), Organisation (SMEs, Large Enterprises), Deployment model (Public Cloud, Private Cloud, Hybrid Cloud), Component (Platform, Services)

Analyst at AMA have conducted special survey and have connected with opinion leaders and Industry experts from various region to minutely understand impact on growth as well as local reforms to fight the situation. A special chapter in the study presents Impact Analysis of COVID-19 on Global Cloud Microservices Market along with tables and graphs related to various country and segments showcasing impact on growth trends.

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa
Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Strategic Points Covered in Table of Content of Global Cloud Microservices Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Microservices market
Chapter 2: Exclusive Summary – the basic information of the Cloud Microservices Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the Cloud Microservices
Chapter 4: Presenting the Cloud Microservices Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying market size by Type, End User and Region 2015-2020
Chapter 6: Evaluating the leading manufacturers of the Cloud Microservices market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries (2021-2026).
Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

Finally, Cloud Microservices Market is a valuable source of guidance for individuals and companies in decision framework.

Data Sources & Methodology

The primary sources involves the industry experts from the Global Cloud Microservices Market including the management organizations, processing organizations, analytics service providers of the industry’s value chain. All primary sources were interviewed to gather and authenticate qualitative & quantitative information and determine the future prospects.

In the extensive primary research process undertaken for this study, the primary sources – Postal Surveys, telephone, Online & Face-to-Face Survey were considered to obtain and verify both qualitative and quantitative aspects of this research study. When it comes to secondary sources Company’s Annual reports, press Releases, Websites, Investor Presentation, Conference Call transcripts, Webinar, Journals, Regulators, National Customs and Industry Associations were given primary weight-age.

What benefits does AMA research study is going to provide?
• Latest industry influencing trends and development scenario
• Open up New Markets
• To Seize powerful market opportunities
• Key decision in planning and to further expand market share
• Identify Key Business Segments, Market proposition & Gap Analysis
• Assisting in allocating marketing investments

Definitively, this report will give you an unmistakable perspective on every single reality of the market without a need to allude to some other research report or an information source. Our report will give all of you the realities about the past, present, and eventual fate of the concerned Market.

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Southeast Asia.

Craig Francis (PR & Marketing Manager)
AMA Research & Media LLP
Unit No. 429, Parsonage Road Edison, NJ
New Jersey USA – 08837
Phone: +1 (206) 317 1218
sales@advancemarketanalytics.com

Advance Market Analytics is Global leaders of Market Research Industry provides the quantified B2B research to Fortune 500 companies on high growth emerging opportunities which will impact more than 80% of worldwide companies’ revenues.
Our Analyst is tracking high growth study with detailed statistical and in-depth analysis of market trends & dynamics that provide a complete overview of the industry. We follow an extensive research methodology coupled with critical insights related industry factors and market forces to generate the best value for our clients. We Provides reliable primary and secondary data sources, our analysts and consultants derive informative and usable data suited for our clients business needs. The research study enables clients to meet varied market objectives a from global footprint expansion to supply chain optimization and from competitor profiling to M&As.

The post Cloud Microservices Market Will Hit Big Revenues In Future | IBM, Contino, AWS appeared first on Artificial Intelligence.

Amazon Web Services (AWS) has revealed new and updated courses for partners in the areas of advanced migration, security governance and machine learning.  

Among the new courses the cloud vendor has made available via its AWS Training and Certification function is AWS Solutions Training for Partners: Advanced Migrating to AWS (Technical), an advanced course that provides prescriptive guidance on how to migrate workloads at scale.  

This course also covers common migration patterns and challenges in large-scale migrations.  

With this course, partners will learn how to identify use cases for re-hosting, re-platforming and re-factoring legacy applications to AWS Cloud. Partners will also learn how to determine which applications are good candidates for automated modernisation.

Also available is the AWS Solutions Training for Partners: Security Governance at Scale (Business) course, which is designed to help sales and business professionals learn how to qualify customer opportunities around security governance at scale.  

This course covers topics such as AWS account management, cost control, security and compliance through automation, along with best practices for establishing an automated, simplified, and secure framework for governance at scale.  

AWS has also launched a major update to its AWS Solutions Training for Partners: Machine Learning on AWS (Business) digital course.

This update will see partners learn how to identify and qualify customer machine learning (ML) opportunities, how to help customers navigate the ML solution-building process and how to use such experience to build an AWS ML solutions practice.

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Leveraging Artificial Intelligence to Create Impressive Products for Disabled People

Technology is an excellent way to enhance the lives of people with disabilities. With the advent of artificial intelligence, several avenues of research have opened up that focus on enhancing the lives of people with impairment.

For instance, Facebook has designed an AI tool that can help the blind “see” again. This AI model explains the images on the Facebook feed of a blind person, so the person using the screen reader gets an idea of what is going on in the picture. This means people with visual impairment no longer have to hear a screen reader say “Photo” by “John Doe.” Google’s ‘Look to Speak’ app uses machine learning and computer vision to allow users to control their devices with their eyes

Similarly, OrCam, a Jerusalem-based company, has developed an AI-based called OrCam Read. This handheld device can read full pages or screens of text aloud from any printed or digital surface, including newspapers, books, product labels, and computers and smartphones. Through this device, OrCam aims to help people with reading challenges, such as dyslexia, mild to moderate vision loss, reading fatigue, as well as for those who read large volumes of text.

Even company giants like Microsoft have started a five-year program called ‘AI for Accessibility,’ with an investment of US$25 million, aiming to put AI in the hands of developers to make the world more accessible by providing AI solutions for the specially-abled. Artificial intelligence not only assists people with physical disabilities but is also helping people struggling with learning problems and mental health issues. E.g., Microsoft’s Windows Hello uses biometric login, i.e., fingerprint, face, or iris, which can work for people with physical disabilities or those with dyslexia who might struggle to remember passwords. AI chatbots like Woebot and Wysa are ensuring the availability of consultation for mental health woes, beyond the therapist hours 24/7.

Meanwhile, people suffering from epilepsy can have seizures from blinking lights and animations. This is why accessiBe, a web accessibility platform enables epileptic users to disable various types of animation, such as GIFs and videos so that they can browse the web without complications. Voiceitt is an app for people with speech impediments, including both those who need it temporarily after strokes and brain injuries, and those with more long-term conditions like cerebral palsy, Parkinson’s, and Down’s syndrome. The app uses machine learning to pick up speakers’ unique speech patterns, recognize any mispronunciations, and rectify them before creating an audio or text output. Livio AI, developed by Starkey, an AI medical device company, is a hearing aid that will enhance the hearing experience by quieting all the external noise from the environment and tracking health-related data to enable patients to seek help during emergencies.

Thanks to artificial intelligence, autonomous vehicles also promise to provide people with disabilities more mobility than ever before. Once the self-driving vehicles are fully integrated into society, they can be a resourceful asset for people with different disabilities, including motor impairment. These people would no longer be dependent on other people or public transport.

Further, most of the existing testing methods are highly ineffective at pinpointing learning disabilities like dyslexia or dyscalculia. Artificial Intelligence can help teachers and healthcare professionals diagnose early signs of such conditions and help the students accordingly. For instance, Australian startup Dystech has developed a screening app for early detection of such learning disorders.

Built on Amazon Web Services (AWS), Dystech employs artificial intelligence and machine learning to screen test if the user has dyslexia or dysgraphia. For the former, the app uses datasets of audio recording from both dyslexic and non-dyslexic adults and children to train the AI and relies on users reading aloud words that appear on the screen while being recorded using their smart device during assessment . And for dysgraphia it uses a photo of a handwritten text for screening. After subjected to a 10-minute screening test, app informs users about their likelihood of having dyslexia or dysgraphia.

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Sumo Logic broadened its observability tools this week, positioning itself as a user-friendly, lower-cost alternative to more established enterprise rivals.

The company’s AWS Observability Solution and Microservices Observability Solution for Kubernetes, first announced in early August, is now generally available. Observability is a term for IT monitoring techniques that automatically surface useful signals amid high volumes of varied data and make detailed custom queries into system performance for troubleshooting.

In the past, Sumo Logic had a less unified approach: individual AWS services displayed log monitoring data in separate dashboards, and alerts on log data were set separately from metrics. The new tools also build in distributed tracing data based on the OpenTelemetry open source project.

The new Sumo Logic observability tools also introduce the vendor’s first automated root cause analysis capabilities, which can proactively pinpoint issues that lead to errors in dependent systems and alert IT ops pros to their presence. Such features have been offered by top competitors such as Splunk and Elastic since 2018. However, Sumo Logic aims to undercut Splunk on price and challenge both with ease of use, as economic pressure on IT departments mounts amid the COVID-19 pandemic.

One user says Sumo Logic has found the right formula.

“I can build [Sumo Logic monitoring] into services within the CI/CD pipeline through Terraform, and logs and metrics flow automatically to standard dashboards that cover about 80% of use cases,” said Andy Makings, head of DevSecOps at Snoop, a U.K. fintech startup. For the rest, setting up custom queries doesn’t require a steep learning curve.

“Even if you don’t know what you’re looking for, you can just put a keyword into search,” Makings said.

The new AWS Observability dashboards still represent a big improvement over Sumo Logic’s previous approach, according to Makings. That approach required separate alerting for log and metrics data, and displayed each AWS service separately rather than correlating data between them.

Sumo Logic observability tempts on price

As cloud-native apps and microservices architectures grow more complex, users must ingest ever-growing volumes of data, which can quickly drive data storage costs out of control. All observability vendors face this problem but take different approaches to solving it.

Makings said he’s used most major IT observability tools in past jobs but finds Sumo Logic the most affordable, even compared with AWS CloudWatch Insights. He declined to give specific cost numbers but said excessive CloudWatch Logs Insights searches had caused monthly AWS bills to balloon unexpectedly in the past.

“The benefit with Sumo is we don’t have to worry about that,” Makings said. Sumo Logic includes LogReduce, which summarizes back-end log data to better pinpoint searches, rather than running potentially costly queries over terabytes of data. “We can afford to ingest everything, which means we can see everything, and scan across all our environments.”

Large enterprise users of Splunk have voiced concerns about cost in the past, though the IT operations management bellwether has made changes over the last year in response. It introduced new entry-level Rapid Adoption pricing options for its on-premises and Splunk Cloud SaaS products in September 2019 that start at $10,000 for three to five use cases, which include IT ops and security.

At the same time, Splunk introduced pricing options based on compute capacity consumed, called infrastructure-based pricing, rather than data volume ingested; and predictive pricing, which is meant to mitigate cost overruns enterprise customers sometimes encountered when they tried to ingest more than their monthly allotment of data.

Users of Splunk Cloud on AWS pay for only infrastructure charges, not for the Splunk software itself. Still, the full Splunk Enterprise suite starts at $1,800 per ingested gigabyte per year, with volume discounts available.

Large IT organizations have found some pricing relief compared with traditional Splunk Enterprise in Elastic, which does not charge separately for its log analytics and SIEM tools. But Sumo Logic similarly bundles SecOps and IT ops tools into its Enterprise Suite, with pricing starting at $4.75 per gigabyte per month, with additional charges for unlimited search starting at $2.14 per gigabyte per month.

The Sumo Logic AWS Observability and Microservices Observability products also don’t come with a separate license cost for software — existing users will be charged for only any additional data they ingest to use the new features. Finally, Sumo Logic users can designate some data for infrequent access and store it for $0.10 per gigabyte per month.

Catching up to observability giants

Sumo’s not alone offering automated root cause analysis capabilities, or in expanding the data sets it can collect. Competitors such as Dynatrace, New Relic, Datadog and Cisco’s AppDynamics have all made similar moves in the last 18 months. New Relic also drastically slashed its pricing for AIOps software in July.

Enterprises are already bombarded by marketing from this glut of observability vendors, and Sumo Logic must fight to develop and maintain its own customer and partner relationships amid the noise, according to one analyst.

“Cost is one big component to [competing], but the crux of it is, are they able to have a conversation with the appropriate parties within a [customer] organization?” said Stephen O’Grady, principal analyst and co-founder at RedMonk in Portland, Maine. “These [updates] are about making sure Sumo Logic continues to have credible conversations with partners [such as AWS] about complicated problems, and that they’re going to give them the attention they deserve.”

While observability and automated root cause analysis are much-hyped features, actual production use isn’t yet mainstream, O’Grady said.

“Nobody’s in a position to own the entire market or a majority of the market yet,” he said. “Sumo Logic is evolving as it needs to, but it’s a crowded market and will continue to be for the foreseeable future.”

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Businesses today run on information and insights, which in turn, run on data. And to study this data that generally exists in an unstructured format, we need machine learning. Next, we also need AI to provide personalized services on a massive scale. However, the problem is that mastering machine learning is super hard and requires a time investment. Meanwhile, though businesses need decision-making insights about their customers, products, and usage to stay relevant in market flux, more than ever, for employees, it is frustrating to write SQL queries. Along with that, waiting on web engineers who would try to figure out algorithms proved quite arduous. Therefore, in a bid to ease such woes, companies are using NoCode platforms to deploy AI and machine learning models, giving them the ability to classify, extract, and analyze organizational data quickly. The NoCode tools are extremely easy to use with results on any query returned in under a minute. Users simply need to upload their dataset from CSV, databases, or CRMs and then get a Google-like search bar to ask a question in natural language. This enables them to create web and mobile apps by dragging and dropping elements, without getting bogged down in code.

While this sounds like a new concept, No- and low-code software development is just an extension of how programming has always evolved toward a more intuitive form. For instance, a spreadsheet is a kind of no- or low-code platform, enabling a user to analyze and manipulate data without writing hardly any code. These development platforms can help users unlock new opportunities worldwide to accelerate the promise of software sustaining the digital world by helping experts work more effectively and giving more people the ability to write software. Drivers of this trend arise from companies’ intention to shift from traditional IT ecosystem and engineering teams into the line of business teams, increase productivity, and reduce costs due to more cloud-based applications. It is important to note that NoCode platforms can never completely replace hand-coded systems. Instead, they can help boost smaller, less experienced data science teams and prototyping for professional data scientists.

The NoCode platforms are proving to be the next battle frontier of the tech giants. Also, they are easily customizable. Analytics Insight brings this month’s top No-Code Machine Learning platforms.

Create ML: Apple’s no-code drag and drop tool, CreateML is the best platform for iOS developers. While initially launched with Xcode, today, CreateML is an independent macOS application that comes with a bunch of pre-trained model templates. In this, one can fine-tune the metrics and set customized iteration count before starting the training. It provides realtime results on the validation data for models such as style transfer. In the end, it’ll generate a CoreML model that can be tested and deployed in iOS applications. It also permits to build models for object detection, activity, and sound classification. It also allows users to train their models from Apple with custom data and enhance performance using an external graphics processing unit.

Teachable Machine: Developed by Google, Teachable Machine is a browser-based system that records using the user’s computer’s webcam and microphone. This platform enables users to train models to recognize images, sounds quickly, and poses right from the browser. In this manner, one can teach the models to classify images, sound recordings, and body postures. The app provides the liberty of using files as well as capturing live examples from the spot. It runs on the Tensorflow.js library in the browser and ensures that the training data stays on the device.

Google AutoML:  This NoCode platform from Google Cloud is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It encompasses vision, natural language, AutoML translation, video intelligence, and tables. It removes the need to know transfer learning or how to create a neural network by providing out of the box support for thoroughly tested deep learning models.

Accelerite ShareInsights by Amazon Web Services (AWS): This NoCode tool allows users to design ETL pipelines without programming. A user can design ETL pipelines without programming, use cloud-native technologies, and create interactive dashboards in a matter of minutes. With the help of AWS services, the tool features a drag and drop console to create the pipeline. Accelrite Shareinsights is a complete data analytics platform for data on S3 or Redshift and also can optimize across Glue, EMR, Sagemaker, and Redshift Spectrum and enables anyone to use cloud-native technologies for creating interactive dashboards. Further, it provides end-to-end data preparation, OLAP, Data lake visual explorer, and machine learning as a single integrated process.

Obviously AI: This no-code tool allows users to upload their datasets from CSV, databases, or CRMs to summon a Google-like search bar to ask a question in natural language. The platform trains the machine learning model by choosing the right algorithm as per the user. It lets companies integrate data from other sources as well, such as MySQL, Salesforce, RedShift, and practice predictive analysis on business data. This tool was founded on the belief that business users should be able to get insights from their data, without waiting on an engineer. Currently, it supports CSV, MySQL, PostgreSQL, BigQuery, Redshift, Salesforce, and Hubspot.

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Amazon Web Services (AWS), an Amazon.com company, announced that Arcelik, a leading manufacturers of household appliances, has selected AWS as its cloud provider for machine learning and analytics across its operations.

Arcelik, which owns 12 global appliance brands including Beko and Grundig, is relying on AWS services to transform itself into a data-driven organization and provide enhanced customer experiences, innovate new services, and lower costs across its operations in nearly 150 countries.

By adopting AWS analytics, compute, database services, Internet of Things (IoT), machine learning, and storage, Arcelik is gaining deeper visibility across its organization, optimizing processes related to production and quality control, and driving efficiency across all business segments, from customer care to manufacturing.

Arcelik is establishing an analytics and machine learning program, powered by AWS. This will aggregate and examine data from its supply chain, maintenance, manufacturing, quality control, and sales operations to achieve predictive insights for evaluating customer satisfaction ratings, forecasting growth, and managing inventory.

Arcelik began this program by building a data lake with Amazon Simple Storage Service (Amazon S3) using AWS Lake Formation, for quickly ingesting, cataloging, cleaning, and securing data, and AWS Glue, for preparing and loading data for analytics.

Arcelik is expanding its use of AWS analytics, IoT, and machine learning services to its factories and product lines, building on a project to develop cloud-connected appliances. The company collects data from more than 1 million deployed smart appliances, using AWS IoT Core to easily and securely connect these appliances to the cloud and other devices.

It also uses Amazon Elasticsearch Service to gain insights from this machine-generated data, enabling Arcelik to analyze consumer usage patterns and diagnose potential performance issues remotely.

Arcelik is rolling out solutions on its manufacturing lines, using machine learning models built with Amazon SageMaker, to identify process improvements and enhance quality control.

Arcelik developed a Smart Component Matching solution on AWS, enabling the company to examine real-time and historical production line data with service data to help the factory’s automatic-production system identify the product parts that are compatible with other components, resulting in better production quality.

Arcelik is implementing these solutions at its washing machine factory in Romania, which was recently included in the World Economic Forum’s ‘Global Lighthouse Network’ – an initiative recognizing some of the world’s most innovative and advanced manufacturing facilities. The company plans to work with AWS to roll out these solutions to additional Arcelik factories.

“AWS is a strategic resource in Arcelik’s digital transformation and global growth, by keeping us agile and solidifying our position as an industry pioneer through the use of advanced analytics, IoT, and machine learning services,” said Utku Barış Pazar, Arcelik’s chief strategy and digital officer.

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Amazon Web Services partnered with social media platform developer RallyPoint, the Department of Veterans Affairs and Harvard University to develop a machine learning model that can detect mental health issues among veterans, Nextgov reported Friday.

Under an agreement with VA, the Amazon Machine Learning Solutions Lab worked with RallyPoint and Harvard’s Nock Lab mental health professionals to utilize data science for identifying “high-value use cases” among users of the military-focused social network.

AWS provided data-labeling services as well as its SageMaker managed-service platform to train a machine-learning model to detect signs of risk in anonymous public posts on RallyPoint.

The team is accepting feedback on the effort and plans to further develop the model in the coming months. They also seek to provide RallyPoint users with access to mental health programs, support groups and hotlines, according to the report.

Dave Gowel, CEO of RallyPoint, said the effort is aimed at supporting government efforts to address veterans’ mental health issues through programs such as VA’s Suicide Prevention Program and the President’s Roadmap to End a National Tragedy of Suicide.

The team effort builds on RallyPoint’s memorandum of understanding with VA to improve veteran interactions with communities signed in 2018.

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